发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出接触中心的重定向方法HOI-Retarget，通过窗口化轨迹优化将人-物交互迁移到类人机器人，实现大规模运动数据生成，并支持跨尺寸增强、单目视频接触吸收及多机器人协作。
AI 中文摘要
从示范中学习（LfD）使类人机器人能够获取多样化的全身技能，但将该范式扩展到人-物交互（HOI）受限于机器人兼容的交互参考的可用性。我们提出了HOI-Retarget，一种接触中心的重定向方法，将人-物交互迁移到类人机器人上，用于大规模运动数据生成。其窗口化轨迹优化将每个标注的接触作为物体坐标系中的目标，在机器人运动学限制下平衡身体跟踪、足部支撑和平滑性。该方法可跨物体尺寸增强单个示范，吸收从单目视频重建的接触，并扩展到多个机器人操作同一物体。我们公开发布了代码和重定向运动数据集。
英文摘要
Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI onto a humanoid robot for large-scale motion-data generation. Its windowed trajectory optimization uses every labeled contact as a target in the object frame, balancing body tracking, foot support and smoothness under the robot's kinematic limits. The method can augment a single demonstration across object sizes, absorb contacts reconstructed from monocular video, and extend to several robots manipulating one object. We publicly release the code and the retargeted motion dataset.
Comments8 pages, 6 figures. Submitted to ICRA. Website: http://shinben0327.github.io/hoi-retarget
